CVE-2021-41219High· 7.8▾ TwilightUndefined behavior via `nullptr` reference binding in sparse matrix multiplication
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 42.9 · likelihood 0 · exploitation 0
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Exploit-prediction probability, daily snapshots since Jul 8.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.2%
0.2% → 0.2%
The code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to nullptr:
import tensorflow as tf
tf.raw_ops.SparseMatMul(
a=[[1.0,1.0,1.0]],
b=[[],[],[]],
transpose_a=False,
transpose_b=False,
a_is_sparse=False,
b_is_sparse=True)
This occurs whenever the dimensions of a or b are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access.
We have patched the issue in GitHub commit e6cf28c72ba2eb949ca950d834dd6d66bb01cfae.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
tensorflow >= 2.6.0, < 2.6.1tensorflow >= 2.5.0, < 2.5.2tensorflow < 2.4.4tensorflow-cpu >= 2.6.0, < 2.6.1tensorflow-cpu >= 2.5.0, < 2.5.2tensorflow-cpu < 2.4.4tensorflow-gpu >= 2.6.0, < 2.6.1tensorflow-gpu >= 2.5.0, < 2.5.2tensorflow-gpu < 2.4.4Upgrade to a patched release:
tensorflow 2.6.1tensorflow 2.5.2tensorflow 2.4.4tensorflow-cpu 2.6.1tensorflow-cpu 2.5.2tensorflow-cpu 2.4.4tensorflow-gpu 2.6.1tensorflow-gpu 2.5.2tensorflow-gpu 2.4.4Connected by shared product, vendor, weakness, or advisory.
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